ISP Lab06

Nguyễn Quốc Bảo - 18110053

Faculty of Mathematics and Computer Science,

University of Science, HCMC

import numpy as np
import cv2
from matplotlib import pyplot as plt
from skimage.color import rgb2gray
from skimage.filters import threshold_otsu
from skimage.measure import label, regionprops
from skimage.segmentation import mark_boundaries
from scipy import ndimage as ndi
import pandas as pd
import json
import os
import timeit
import random

def ShowImage(ImageList, nRows = 1, nCols = 2, WidthSpace = 0.00, HeightSpace = 0.00):
    from matplotlib import pyplot as plt
    import matplotlib.gridspec as gridspec
    
    gs = gridspec.GridSpec(nRows, nCols)
    gs.update(wspace=WidthSpace, hspace=HeightSpace) # set the spacing between axes.
    plt.figure(figsize=(20,20))
    for i in range(len(ImageList)):
        ax1 = plt.subplot(gs[i])
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        ax1.set_aspect('equal')
        
        plt.subplot(nRows, nCols,i+1)
        
        image = ImageList[i].copy()
        if (len(image.shape) < 3):
            plt.imshow(image, plt.cm.gray)
        else:
            plt.imshow(image)
            plt.title("Image " + str(i))
            plt.axis('off')
    plt.show()

def morphology(Mask, Size):
    from skimage.morphology import erosion, dilation, opening, closing, white_tophat
    from skimage.morphology import disk
    selem = disk(abs(Size))
    if(Size > 0):
        result = dilation(Mask, selem)
    else:
        result = erosion(Mask, selem)
    return result

import os
import pandas as pd
def get_subfiles(dir):
    "Get a list of immediate subfiles"
    return next(os.walk(dir))[2]

def SegmentColorImageByMask(IM, Mask):
    Mask = Mask.astype(np.uint8)
    result = cv2.bitwise_and(IM, IM, mask = Mask)
    return result
def SegmentationByOtsu(image, mask):
    image_process = image.copy()
    image_mask = mask.copy()
    image_process[image_mask == 0] = 0
    ListPixel = image_process.ravel()
    ListPixel = ListPixel[ListPixel > 0]
    from skimage.filters import threshold_otsu
    otsu_thresh = threshold_otsu(ListPixel)
    return otsu_thresh
def SegmentByKmeans(image_orig, nClusters = 3):

    img = image_orig.copy()
    Z = img.reshape((-1,3))
    # convert to np.float32
    Z = np.float32(Z)
    # define criteria, number of clusters(K) and apply kmeans()
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 1.0)
    K = nClusters
    ret,labellist,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
    # Now convert back into uint8, and make original image
    center = np.uint8(center)
    res = center[labellist.flatten()]
    res2 = res.reshape((img.shape))
    label2 = labellist.reshape((img.shape[:2]))
    
    image_index = label2
    image_kmeans = res2
    # Sort to make sure the index is stable
    AreaList = []
    for idx in range(image_index.max() + 1):
        mask = image_index == idx
        AreaList.append(mask.sum().sum())
        
    sort_index = np.argsort(AreaList)[::-1]
    index = 0
    image_index1 = image_index * 0
    for idx in sort_index:
        image_index1[image_index == idx] = index
        index = index + 1
    image_index = image_index1.copy()
    return image_index, image_kmeans

def ResizeImage(IM, DesiredWidth, DesiredHeight):
    from skimage.transform import rescale, resize
    OrigWidth = float(IM.shape[1])
    OrigHeight = float(IM.shape[0])
    Width = DesiredWidth
    Height = DesiredHeight
    if((Width == 0) & (Height == 0)):
        return IM
    if(Width == 0):
        Width = int((OrigWidth * Height)/OrigHeight)
    if(Height == 0):
        Height = int((OrigHeight * Width)/OrigWidth)
    dim = (Width, Height)
    # print(dim)
    resizedIM = cv2.resize(IM, dim, interpolation = cv2.INTER_NEAREST)
    # imshows([IM, resizedIM], ["Image", "resizedIM"],1,2)
    return resizedIM

def LabelObjectByMask(image_input, image_mask, type = "BBox", color = (0,255,0), thick = 2):

    image_input = image_orig.copy()
    image_output = image_input.copy()
    label_img = label(image_mask)
    regions = regionprops(label_img)
    for props in regions:
        minr, minc, maxr, maxc = props.bbox
        left_top = (minc, minr)
        right_bottom = (maxc, maxr)
        at_row, at_col = props.centroid
        if(type == "Center"):
            cv2.drawMarker(image_output, (int(at_col), int(at_row)),color, markerType=cv2.MARKER_STAR,\
                       markerSize=15, thickness= 1, line_type=cv2.LINE_AA)
        if(type == "BBox"):
            cv2.rectangle(image_output,left_top, right_bottom, color ,thick)
    
    if(type == "Boundary"):
        color = [(number / 255) for number in color]
        image_mask = morphology(image_mask, 1)
        image_output = mark_boundaries(image_output, image_mask, color = color, mode='thick')
    
    if(type == "Fill"):
        image_output[image_mask > 0] = color
    return image_output

def SelectMaskByThreshArea(Mask, minArea = 300, maxArea = 100000):

    import pandas as pd
    from skimage.measure import label, regionprops
    mask = Mask.copy()
    mask_output = mask * 0
    bboxList = []
    
    label_img = label(mask)
    regions = regionprops(label_img)
    for props in regions:
        area = props.area
        label = props.label
        if((area > minArea) and (area < maxArea)):
            mask_output = mask_output + (label_img == label).astype(int)
    return mask_output

DataPath = "Lab06 - Image/"
path = DataPath
all_names = get_subfiles(path)
print("Number of Images:", len(all_names))
IMG = []
for i in range(len(all_names)):
    tmp = cv2.imread(path + all_names[i])
    IMG.append(tmp)
SegDataIMG = IMG.copy()
SegDataName = all_names

Number of Images: 60

Vết Defect A

FileName = 'DefectA 03.bmp'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)


h = image_hsv[:,:,0]
s = image_hsv[:,:,1]
v = image_hsv[:,:,2]
y = image_ycbcr[:,:,0]
cb = image_ycbcr[:,:,1]
cr = image_ycbcr[:,:,2]


image_index, image_kmeans = SegmentByKmeans(image_orig, nClusters = 4)

mask_list = []
mask_abnormal = image_gray * 0
for idx in range(image_index.max() + 1):
    imask = image_index == idx
    mask_small = SelectMaskByThreshArea(imask, minArea = 200, maxArea = 2000)
#     mask_small=SegmentColorImageByMask(image_index, imask)
    mask_list.append(imask)
    mask_abnormal = mask_abnormal + mask_small
ShowImage(mask_list, 1, len(mask_list))
ShowImage([mask_abnormal], 1, 3)

Selected Image :  
Index  4 
Name  DefectA 03.bmp

imask = image_index == 3
label_img = label(imask)
# label_img = label(mask_abnormal)
regions = regionprops(label_img, intensity_image = image_hsv[:,:,0])

mask_condition1 = mask_abnormal * 0

for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel
    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity
    condition1 = (area > 20) and (area < 2000)
#     condition2 = (area > 20) and (area < 2000)

    if(condition1):
        mask_condition1 = mask_condition1 + (imask).astype(int)

image_output1 = LabelObjectByMask(image_orig, mask_condition1, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition1, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition1, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition1, type = "Boundary", color = (0,255,0), thick =
2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

Vết Defect B

FileName = 'DefectB 04.bmp'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)


h = image_hsv[:,:,0]
s = image_hsv[:,:,1]
v = image_hsv[:,:,2]
y = image_ycbcr[:,:,0]
cb = image_ycbcr[:,:,1]
cr = image_ycbcr[:,:,2]


image_index, image_kmeans = SegmentByKmeans(image_orig, nClusters = 4)

mask_list = []
mask_abnormal = image_gray * 0
for idx in range(image_index.max() + 1):
    imask = image_index == idx
#     mask_small = SelectMaskByThreshArea(imask, minArea = 200, maxArea = 2000)
    mask_small=SegmentColorImageByMask(image_index, imask)
    mask_list.append(imask)
    mask_abnormal = mask_abnormal + mask_small
ShowImage(mask_list, 1, len(mask_list))
ShowImage([mask_abnormal], 1, 3)

Selected Image :  
Index  10 
Name  DefectB 04.bmp

# imask = image_index == 3
# label_img = label(imask)
label_img = label(mask_abnormal)
regions = regionprops(label_img, intensity_image = image_hsv[:,:,0])

mask_condition1 = mask_abnormal * 0

for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel
    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity
    condition1 = (area > 1000) and (area < 2000)
    condition2 = (area > 100) and (area < 800)

    if(condition1 or condition2):
        mask_condition1 = mask_condition1 + (imask).astype(int)
mask_condition1 = morphology(mask_condition1,1)
image_output1 = LabelObjectByMask(image_orig, mask_condition1, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition1, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition1, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition1, type = "Boundary", color = (0,255,0), thick =
2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

Biển số xe

FileName = 'DrivingPlate 03.jpg'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)


h = image_hsv[:,:,0]
s = image_hsv[:,:,1]
v = image_hsv[:,:,2]
y = image_ycbcr[:,:,0]
cb = image_ycbcr[:,:,1]
cr = image_ycbcr[:,:,2]


image_index, image_kmeans = SegmentByKmeans(image_orig, nClusters = 5)

mask_list = []
mask_abnormal = image_gray * 0
for idx in range(image_index.max() + 1):
    imask = image_index == idx
    mask_small = SelectMaskByThreshArea(imask, minArea = 0, maxArea = 1000)
#     mask_small=SegmentColorImageByMask(image_index, imask)
    mask_list.append(imask)
    mask_abnormal = mask_abnormal + mask_small
    
mask_abnormal = morphology(mask_abnormal, -2)
ShowImage(mask_list, 1, len(mask_list))
ShowImage([mask_abnormal], 1, 3)

Selected Image :  
Index  14 
Name  DrivingPlate 03.jpg

# imask = image_index == 3
# label_img = label(imask)
label_img = label(mask_abnormal)
regions = regionprops(label_img, intensity_image= image_gray)

mask_condition1 = mask_abnormal * 0

for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity

    minr, minc, maxr, maxc = props.bbox
    width = maxc - minc
    height = maxr - minr
    ratewh = width/height

    condition1 = (imean > 150) and (imin < 70) and (ratewh > 1.4) and (ratewh < 1.8)
    condition2 = props.major_axis_length > 20

    if(condition1 and condition2):
        mask_condition1 = mask_condition1 + (imask).astype(int)

image_output1 = LabelObjectByMask(image_orig, mask_condition1, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition1, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition1, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition1, type = "Boundary", color = (0,255,0), thick =2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

Vết xuất huyết và xuất tiết

FileName = 'Eye 05.jpg'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)

h = image_hsv[:,:,0]
s = image_hsv[:,:,1]
v = image_hsv[:,:,2]
y = image_ycbcr[:,:,0]
cb = image_ycbcr[:,:,1]
cr = image_ycbcr[:,:,2]

image_index, image_kmeans = SegmentByKmeans(image_orig, nClusters = 5)
# ShowImage([image_orig, image_index, image_kmeans], 1, 3)
mask_list = []
mask_abnormal = image_gray * 0

for idx in range(image_index.max() + 1):
    imask = image_index == idx
    mask_small = SelectMaskByThreshArea(imask, minArea = 0, maxArea = 2000)
    mask_list.append(imask)
    mask_abnormal = mask_abnormal + mask_small
ShowImage(mask_list, 1, len(mask_list))
ShowImage([mask_abnormal], 1, 3)

Selected Image :  
Index  20 
Name  Eye 05.jpg

label_img = label(mask_abnormal)
regions = regionprops(label_img, intensity_image=cr)
mask_condition1 = mask_abnormal * 0
mask_condition2 = mask_abnormal * 0
for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity

    condition1 = (area > 5) and (area < 5000) and (imean < 70)
    condition2 = (area > 5) and (area < 300) and (imean > 80)

    if(condition1):
        mask_condition1 = mask_condition1 + (imask).astype(int)

    if(condition2):
        mask_condition2 = mask_condition2 + (imask).astype(int)
image_output1 = LabelObjectByMask(image_orig, mask_condition1, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition1, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition1, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition1, type = "Boundary", color = (0,255,0), thick =
2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

image_output1 = LabelObjectByMask(image_orig, mask_condition2, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition2, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition2, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition2, type = "Boundary", color = (0,255,0), thick = 2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

Khuôn mặt người

FileName = 'Face 04.jpg'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)

lower = np.array([0, 39, 80], dtype = "uint8")
upper = np.array([15, 240, 255], dtype = "uint8")
skinMask = cv2.inRange(image_hsv, lower, upper)
ShowImage([image_orig,skinMask],1,3)

Selected Image :  
Index  24 
Name  Face 04.jpg

label_img = label(skinMask)
regions = regionprops(label_img, intensity_image= image_ycbcr[:,:,1])
mask_condition = skinMask * 0
for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity
    app_std = (imax - imin)/4

    minr, minc, maxr, maxc = props.bbox
    width = maxc - minc
    height = maxr - minr
    ratewh = width/height
    rateArea = area/ (width * height)

    condition = (area > 2000) and (area < 9000) and (ratewh < 1.5) and (rateArea > 0.1)
    if(condition):
        mask_condition = mask_condition + (imask).astype(int)
        print(area, ratewh, rateArea)
# mask_condition = morphology(mask_condition,2)
image_output1 = LabelObjectByMask(image_orig, mask_condition, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition, type = "Boundary", color = (0,255,0), thick = 2
)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

6685 0.46551724137931033 0.47431531148006245
6720 0.6462585034013606 0.48120300751879697
4333 0.608 0.45610526315789474
2804 0.7894736842105263 0.6149122807017544

Lửa

FileName = 'Fire 03.jpg'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)



image_index, image_kmeans = SegmentByKmeans(image_orig, nClusters = 5)

mask_list = []
mask_abnormal = image_gray * 0
for idx in range(image_index.max() + 1):
    imask = image_index == idx
    mask_small = SelectMaskByThreshArea(imask, minArea = 900, maxArea = 3000)
#     mask_small=SegmentColorImageByMask(image_index, imask)
    mask_list.append(imask)
    mask_abnormal = mask_abnormal + mask_small

ShowImage(mask_list, 1, len(mask_list))
ShowImage([mask_abnormal], 1, 3)

Selected Image :  
Index  28 
Name  Fire 03.jpg

label_img = label(mask_abnormal)
regions = regionprops(label_img, intensity_image= image_gray)

mask_condition1 = mask_abnormal * 0

for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity


    condition1 = (((area >900) and (area <1200)) or ((area >1400) and (area < 5000))) and (imean > 150)

    if(condition1):
        mask_condition1 = mask_condition1 + (imask).astype(int)
mask_condition1 = morphology(mask_condition1,3)
image_output1 = LabelObjectByMask(image_orig, mask_condition1, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition1, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition1, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition1, type = "Boundary", color = (0,255,0), thick =2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

Bông hoa

FileName = 'Flower 03.jpg'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)

image_index, image_kmeans = SegmentByKmeans(image_orig, nClusters = 4)

mask_list = []
mask_abnormal = image_gray * 0
for idx in range(image_index.max() + 1):
    imask = image_index == idx
#     mask_small = SelectMaskByThreshArea(imask, minArea = 900, maxArea = 3000)
    mask_small=SegmentColorImageByMask(image_index, imask)
    mask_list.append(imask)
    mask_abnormal = mask_abnormal + mask_small
ShowImage(mask_list, 1, len(mask_list))
ShowImage([mask_abnormal], 1, 3)

Selected Image :  
Index  33 
Name  Flower 03.jpg

label_img = label(mask_abnormal)
regions = regionprops(label_img, intensity_image= image_gray)

mask_condition1 = mask_abnormal * 0

for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity


    condition1 = (area > 200 and area < 23000)  and (imean > 150) 

    if(condition1):
        mask_condition1 = mask_condition1 + (imask).astype(int)

image_output1 = LabelObjectByMask(image_orig, mask_condition1, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition1, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition1, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition1, type = "Boundary", color = (0,255,0), thick =2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

Trái bóng

FileName = 'Football 02.jpg'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)

image_index, image_kmeans = SegmentByKmeans(image_orig, nClusters = 6)

mask_list = []
mask_abnormal = image_gray * 0
for idx in range(image_index.max() + 1):
    imask = image_index == idx
    mask_small=SegmentColorImageByMask(image_index, imask)
    mask_list.append(imask)
    mask_abnormal = mask_abnormal + mask_small
mask_abnormal = morphology(mask_abnormal, -1)
ShowImage(mask_list, 1, len(mask_list))
ShowImage([mask_abnormal], 1, 3)

Selected Image :  
Index  1 
Name  Football 02.jpg

label_img = label(mask_abnormal)
regions = regionprops(label_img, intensity_image= image_gray)

mask_condition1 = mask_abnormal * 0

for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity


    condition1 = (((area >700 )) ) and (imean > 200)
    
    if(condition1):
        mask_condition1 = mask_condition1 + (imask).astype(int)

image_output1 = LabelObjectByMask(image_orig, mask_condition1, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition1, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition1, type = "Center", color = (0,255,0), thick = 3)
image_output4 = LabelObjectByMask(image_orig, mask_condition1, type = "Boundary", color = (0,255,0), thick =2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

Bàn Tay

FileName = 'Hand Gesture 05.jpg'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)

lower = np.array([0, 40, 10], dtype = "uint8")
upper = np.array([23, 218, 245], dtype = "uint8")
skinMask = cv2.inRange(image_hsv, lower, upper)
ShowImage([image_orig,skinMask],1,3)

Selected Image :  
Index  44 
Name  Hand Gesture 05.jpg

label_img = label(skinMask)
regions = regionprops(label_img, intensity_image= image_ycbcr[:,:,1], coordinates='rc')
mask_condition = skinMask * 0
for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity
    app_std = (imax - imin)/4

    minr, minc, maxr, maxc = props.bbox
    width = maxc - minc
    height = maxr - minr
    ratewh = width/height
    rateArea = area/ (width * height)
    
    condition = (area > 1000) and (area < 13000) and (ratewh < 1.5) and (rateArea > 0.3)
    if(condition):
        mask_condition = mask_condition + (imask).astype(int)
        print(area, ratewh, rateArea)
image_output1 = LabelObjectByMask(image_orig, mask_condition, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition, type = "Boundary", color = (0,255,0), thick = 2
)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

/home/qbao/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: The coordinates keyword argument to skimage.measure.regionprops is deprecated. All features are now computed in rc (row-column) coordinates. Please remove `coordinates="rc"` from all calls to regionprops before updating scikit-image.
  

10855 0.5948717948717949 0.47988505747126436

Võng mạc

FileName = 'Iris 03.jpg'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)


lower = np.array([60, 0, 12], dtype = "uint8")
upper = np.array([130, 250, 250], dtype = "uint8")
skinMask = cv2.inRange(image_hsv, lower, upper)
ShowImage([image_orig,skinMask],1,3)

Selected Image :  
Index  47 
Name  Iris 03.jpg

label_img = label(skinMask)
regions = regionprops(label_img, intensity_image= image_ycbcr[:,:,1])
mask_condition = skinMask * 0
for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity
    app_std = (imax - imin)/4

    minr, minc, maxr, maxc = props.bbox
    width = maxc - minc
    height = maxr - minr
    ratewh = width/height
    rateArea = area/ (width * height)
    
    condition = (area > 1000) and (area < 13000) and (ratewh < 1.5) and (rateArea > 0.3)
    if(condition):
        mask_condition = mask_condition + (imask).astype(int)
        print(area, ratewh, rateArea)
mask_condition = morphology(morphology(mask_condition,15),-11)
image_output1 = LabelObjectByMask(image_orig, mask_condition, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition, type = "Boundary", color = (0,255,0), thick = 2
)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

10452 1.3181818181818181 0.6552978056426332

Lá Phổi

FileName = 'Lung 02.png'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)

image_index, image_kmeans = SegmentByKmeans(image_orig, nClusters = 2)

mask_list = []
mask_abnormal = image_gray * 0
for idx in range(image_index.max() + 1):
    imask = image_index == idx
    otsu_thresh = SegmentationByOtsu(image_gray,imask)
    mask_small = -image_gray > otsu_thresh
    mask_list.append(imask)
    mask_abnormal = mask_abnormal + mask_small
ShowImage(mask_list, 1, len(mask_list))
ShowImage([mask_abnormal], 1, 3)

Selected Image :  
Index  51 
Name  Lung 02.png

label_img = label(mask_abnormal)
regions = regionprops(label_img, intensity_image= image_gray)

mask_condition1 = mask_abnormal * 0

for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity

    condition1 = (area > 500)  and (imean > 30)
    if(condition1):
        mask_condition1 = mask_condition1 + (imask).astype(int)

image_output1 = LabelObjectByMask(image_orig, mask_condition1, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition1, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition1, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition1, type = "Boundary", color = (0,255,0), thick =2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)

Vết melanoma trên da

FileName = 'Skin 01.jpg'
idx = SegDataName.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", SegDataName[idx])
image = SegDataIMG[idx]
image_orig = ResizeImage(image, DesiredWidth = 0, DesiredHeight = 350)
image_orig = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)

image_index, image_kmeans = SegmentByKmeans(image_orig, nClusters = 3)

mask_list = []
mask_abnormal = image_gray * 0
for idx in range(image_index.max() + 1):
    imask = image_index == idx
#     mask_small = SelectMaskByThreshArea(imask, minArea = 500, maxArea = 1000)
    mask_small=SegmentColorImageByMask(image_index, imask)
    mask_list.append(imask)
    mask_abnormal = mask_abnormal + mask_small
mask_abnormal = morphology(mask_abnormal, -2)
ShowImage(mask_list, 1, len(mask_list))
ShowImage([mask_abnormal], 1, 3)

Selected Image :  
Index  55 
Name  Skin 01.jpg

label_img = label(mask_abnormal)
regions = regionprops(label_img, intensity_image= image_gray)

mask_condition1 = mask_abnormal * 0

for props in regions:
    area = props.area
    ilabel = props.label
    imask = label_img == ilabel

    imean = props.mean_intensity
    imax = props.max_intensity
    imin = props.min_intensity


    condition1 = (((area >9000 and area < 100000)) ) and (imean > 10)
    condition2 = ((( area > 6500 and area < 10000)) ) and (imean > 10)
    
    if(condition1 or condition2):
        mask_condition1 = mask_condition1 + (imask).astype(int)
        print('area: {}, imean: {}'.format(area, imean))
        ShowImage([imask],1,3)
mask_condition1 = morphology(mask_condition1,2)

area: 6999, imean: 138.18916988141163

area: 66149, imean: 72.93523711620735

image_output1 = LabelObjectByMask(image_orig, mask_condition1, type = "Fill", color = (0,255,0), thick = 2)
image_output2 = LabelObjectByMask(image_orig, mask_condition1, type = "BBox", color = (0,255,0), thick = 2)
image_output3 = LabelObjectByMask(image_orig, mask_condition1, type = "Center", color = (0,255,0), thick = 2)
image_output4 = LabelObjectByMask(image_orig, mask_condition1, type = "Boundary", color = (0,255,0), thick =2)
ShowImage([image_output1, image_output2, image_output3, image_output4], 1, 4)